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편평세포암종 임파절 전이에 대한 인공 신경망 시스템의 진단능 평가
Artificial Neural Network System in Evaluating Cervical Lymph Node Metastasis of Squamous Cell Carcinoma

DC Field Value Language
dc.contributor.author박상욱-
dc.contributor.author허민석-
dc.contributor.author이삼선-
dc.contributor.author최순철-
dc.contributor.author박태원-
dc.contributor.author유동수-
dc.date.accessioned2011-10-12T05:54:29Z-
dc.date.available2011-10-12T05:54:29Z-
dc.date.issued1999-
dc.identifier.citationJ Korean Oral Maxillofac Radiol 1999;29:149-159en
dc.identifier.issn0389-9705-
dc.identifier.urihttp://hdl.handle.net/10371/74043-
dc.description.abstractPurpose: The purpose of this study was to evaluate cervical lymph node metastasis of oral squamous cell carcinoma patients by MRI film and neural network system. Materials and Methods: The oral squamous cell carcinoma patients(21 patients. 59 lymph nodes) who have visited SNU hospital and been taken by MRI. were included in this study. Neck dissection operations were done and all of the cervical lymph nodes were confirmed with biopsy. In MR images. each lymph node were evaluated by using 6 MR imaging criteria(size. roundness. heterogeneity. rim enhancement. central necrosis, grouping) respectively. Positive predictive value. negative predictive value. and accuracy of each MR imaging criteria were calculated. At neural network system. the layers of neural network system consisted of 10 input layer units. 10 hidden layer units and 1 output layer unit. 6 MR imaging criteria previously described and 4 MR imaging criteria (site I-node level II and submandibular area. site II-other node level. shape I-oval. shape II-bean) were included for input layer units. The training files were made of 39 lymph nodes(24 metastatic lymph nodes. 10 non-metastatic lymph nodes) and the testing files were made of other 20 lymph nodes(10 metastatic lymph nodes. 10 non-metastatic lymph nodes). The neural network system was trained with training files and the output level (metastatic index) of testing files were acquired. Diagnosis was decided according to 4 different standard metastatic index-68. 78. 88. 98 respectively and positive predictive values. negative predictive values and accuracy of each standard metastatic index were calculated. Results: In the diagnosis of using single MR imaging criteria. the rim enhancement criteria had highest positive predictive value (0.95) and the size criteria had highest negative predictive value (0.77). In the diagnosis of using single MR imaging criteria. the highest accurate criteria was heterogeneity (accuracy: 0.81) and the lowest one was central necrosis (accuracy: 0.59). In the diagnosis of using neural network systems. the highest accurate standard metastatic index was 78. and that time. the accuracy was 0.90. Neural network system was more accurate than any other single MR imaging criteria in evaluating cervical lymph node metastasis. Conclusion: Neural network system has been shown to be more useful than any other single MR imaging criteria. In future. Neural network system will be powerful aiding tool in evaluating cervical node metastasis.en
dc.description.sponsorship이 논문은 1997년도 서울대학교병원 지정공동연구비지원에 의해 이루어진 것임.en
dc.language.isokoen
dc.publisher대한구강악안면방사선학회en
dc.subjectMRIen
dc.subjectsquamous cell carcinomaen
dc.subjectcervical lymph node metastasisen
dc.subjectartificial neural network systemen
dc.title편평세포암종 임파절 전이에 대한 인공 신경망 시스템의 진단능 평가en
dc.title.alternativeArtificial Neural Network System in Evaluating Cervical Lymph Node Metastasis of Squamous Cell Carcinomaen
dc.typeArticleen
dc.contributor.AlternativeAuthorPark, Sang-Wook-
dc.contributor.AlternativeAuthorHeo, Min-Suk-
dc.contributor.AlternativeAuthorLee, Sam-Sun-
dc.contributor.AlternativeAuthorChoi, Soon-Chul-
dc.contributor.AlternativeAuthorPark, Tae-Won-
dc.contributor.AlternativeAuthorYoo, Dong-Sou-
Appears in Collections:
College of Dentistry/School of Dentistry (치과대학/치의학대학원)Dept. of Dentistry (치의학과)Journal Papers (저널논문_치의학과)
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